「keywords」の検索結果

Coder-agent working-file budget discipline: keep the editable working set (files you /add into writable context) under ~25k tokens, separate "read" from "edit", delegate breadth to a read-only repo-map, and drop files once edited. Use when an LLM coder-agent edits multiple files, when the working set must stay focused, or when the model starts editing the wrong file / missing targets because too much context dilutes attention. Search keywords: context window full, agent edits wrong file, too much context, /add /drop files, working file budget, context dilution, lost in the middle.

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Lifecycle SOP for **per-model prompt artifacts** — the compiled prompts, instructions, few-shot demos, edit-format pins, and embedding-bound indices that change behavior when the underlying LM, dataset, or framework version changes. Activate when adopting compiled prompts (DSPy, GEPA, BootstrapFewShot output), when supporting multiple LMs in production, when a provider deprecates a model snapshot, or when a framework deprecates a config surface (LlamaIndex `ServiceContext` → `Settings`, Aider edit-format defaults). Do NOT activate for one-off raw prompt edits or for truly model-agnostic system prompts that have been swap-tested. Search keywords: prompt portability, model swap, recompile prompt, model deprecation, prompt per model, prompt breaks on new model, version compiled prompts.

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Enhancement overlay — version the WHOLE deployable LLM-app artifact as one bundle: prompts + compiled programs + model snapshot pins + retrieval config + eval-set version, versioned together so a deploy is reproducible and rollback is atomic. Activate when preparing to deploy an LLM app, when asking "what exactly is running in prod right now?", when a deploy must be reproducible months later, or when an incident needs a clean rollback. The core reframe: an LLM app artifact is NOT an ML model — it is a manifest over many independently-mutable parts, not one weights file. Do NOT activate for one-off prompt edits with no deploy, for a single-component demo, or where a vendor owns the whole prompt lifecycle. For versioning ONE compiled prompt use [[agentsop-per-model-artifacts]]; for the CI comparison mechanism use [[agentsop-regression-gate]]. Search keywords: prompt versioning, reproducible deploy, what is running in prod, rollback LLM app, model pinning, prompt registry, version prompts and config.

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短剧-抖音信息源 — 每日扫描抖音短剧爆款内容,按点赞量筛选热门短剧,智能聚类题材方向后生成包含封面、互动数据与创作洞察的HTML日报。支持按题材(穿越/霸总/重生等)、达人、时间范围定向查询。⚠️查询前脚本先做输入校验：关键词需命中短剧题材词库（topic_keywords 中规定的题材名+全部相关词，如「打脸」命中逆袭题材相关词），命中后直接使用该关键词查询数据；不满足时提醒'关键词不满足查询条件'并推荐相关词，且**不发起接口请求**；查询无匹配数据或全量数据不足时先询问用户是否按推荐题材重新查询，确认后才可查询（不自动扩展、不自动降级全量）。日期超出有效查询范围时提醒并自动回退最近有数据日期，无需用户确认。当用户需要短剧抖音日报、抖音短剧爆款、短剧热点、短剧创作趋势或自定义题材查询时使用。

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短剧-公众号信息源 — 每日扫描公众号短剧爆款文章,按阅读量筛选热门内容,智能聚类题材方向后生成包含封面图、互动数据与创作洞察的HTML日报。支持按题材(穿越/霸总/重生等)、公众号、时间范围定向查询。⚠️查询前脚本先做输入校验：关键词需命中短剧题材词库（topic_keywords 中规定的题材名+全部相关词，如「打脸」命中逆袭题材相关词），命中后直接使用该关键词查询数据；不满足时提醒'关键词不满足查询条件'并推荐相关词，且**不发起接口请求**；日期超出有效查询范围时提醒并自动回退最近有数据日期，无需用户确认。**无数据确认制**：查询无匹配数据或数据不足时禁止自动发起任何额外查询（禁止自动降级全量/自动扩展题材），必须先展示推荐题材关键词并等待用户确认后才可查询。当用户需要短剧公众号日报、公众号短剧爆款、短剧热点、短剧创作趋势或自定义题材查询时使用。

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B站短剧爆款内容追踪工具，每日自动扫描B站短剧创作内容，按点赞量筛选爆款作品，智能聚类题材后生成可视化HTML日报与创作趋势分析。⚠️查询前脚本先做输入校验：关键词需命中短剧题材词库（topic_keywords 中规定的题材名+全部相关词，如「打脸」命中逆袭题材相关词），命中后直接使用该关键词查询数据；不满足时提醒'关键词不满足查询条件'并推荐相关词，且不发起接口请求。数据每日15:00更新前一天数据，目标日期无数据时必须先告知用户并等待确认后才能调用接口，禁止自动获取。当用户需要查询B站短剧爆款日报、分析短剧题材趋势、查看热门达人表现或生成短剧创作趋势报告时使用。

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B站关键词搜账号工具。根据用户输入的关键词查询B站相关账号，支持按综合排序或粉丝数排序，返回账号列表（含粉丝数、等级、简介等信息）。当用户需要搜索B站账号、查找B站UP主、找B站同类账号时使用。

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B站关键词搜作品工具。根据用户输入的关键词调用 Redfox 接口搜索B站最新视频，支持按排序方式和发布时间筛选，返回新鲜数据（非缓存/历史数据）。当用户要求搜索B站最新内容、查看某关键词下的视频数据时使用。触发词：B站搜索、B站最新视频、B站热门、B站搜作品、bilibili搜索。

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Use when first encountering an unknown binary, ELF file, executable, or firmware blob. Fast fingerprinting via rabin2 - architecture detection (ARM, x86, MIPS), ABI identification, dependency mapping, string extraction. Keywords - "what is this binary", "identify architecture", "check file type", "rabin2", "file analysis", "quick scan"

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Use when reverse engineering tools are missing, not working, or need configuration. Installation guides for radare2 (r2), Ghidra, GDB, QEMU, Frida, binutils, and cross-compilation toolchains. Keywords - "install radare2", "setup ghidra", "r2 not found", "qemu missing", "tool not installed", "configure gdb", "cross-compiler"

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Use when ready to document findings, generate a report, or summarize binary analysis results. Compiles analysis findings into structured reports - correlates facts from triage/static/dynamic phases, validates hypotheses, generates documentation with evidence chains. Keywords - "summarize findings", "generate report", "document analysis", "what did we find", "write up results", "export findings"

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Use when analyzing binary structure, disassembling code, or decompiling functions. Deep static analysis via radare2 (r2) and Ghidra headless - function enumeration, cross-references (xrefs), decompilation, control flow graphs. Keywords - "disassemble", "decompile", "what does this function do", "find functions", "analyze code", "r2", "ghidra", "pdg", "afl"

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Use when you need to run a binary, trace execution, or observe runtime behavior. Runtime analysis via QEMU emulation, GDB debugging, and Frida hooking - syscall tracing (strace), breakpoints, memory inspection, function interception. Keywords - "run binary", "execute", "debug", "trace syscalls", "set breakpoint", "qemu", "gdb", "frida", "strace", "watch memory"

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Podcast knowledge workflows powered by Podwise CLI: search podcasts and episodes by keyword, monitor followed shows for new releases, find popular episodes, ask questions and extract insights from transcript content, process Podwise episode URLs, YouTube videos, Xiaoyuzhou links, and local audio or video files to retrieve transcripts, summaries, chapters, Q&A, mind maps, highlights, and keywords — plus catch up on your backlog, refine your listening taste, generate weekly recaps, export episode notes to PKM tools, research topics across podcasts, debate episode ideas, and generate language learning cards. Use when the user wants to find, summarize, transcribe, or extract insights from any podcast or audio content, or manage their listening library.

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Efficient database search tool for bioRxiv preprint server. Use this skill when searching for life sciences preprints by keywords, authors, date ranges, or categories, retrieving paper metadata, downloading PDFs, or conducting literature reviews.

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Create style-driven slide images strictly with the Image 2 model, assemble those images into image-only PPTX decks, and manage reusable visual style libraries from documents or visual references. Use when the user asks for a "PPT Skill", "风格驱动 PPT", "提炼风格做 PPT", "调用某个风格做 PPT", "图片版 PPT", "保存 PPT 风格", "列出 PPT 风格", "文档生成 PPT", "文章生成 PPT", "把文档做成演示文稿", or wants to extract, save, reuse, and apply visual style keywords specifically for visual slide/image deck creation.

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Remove the AI tells readers react to in an Instagram caption or carousel slide: 2026 vocabulary by density, reveal bridges, staccato stacks, stacked triads, performed sincerity, emoji storms; caps em dashes. Includes --mode audit (first-125 hook, length, hashtags, emoji, CTA, media) and --mode profile. Not for beating AI detectors (no edit reliably does). Not for writing from scratch (use ig-caption-writer or ig-carousel-planner). Keywords: humanize, de-AI caption, audit before posting.

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Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.

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Access USPTO patent data via PatentsView REST API and Google Patents Public Data (BigQuery). Search by inventor, assignee, CPC, or keywords; download metadata and claims; analyze portfolios; track tech trends. For IP landscape analysis, competitor monitoring, prior art search, and tech forecasting in life sciences and biotech.

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Group related keywords into topic clusters. Create content hub architecture recommendations and internal linking strategies.

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Optimize Apple App Store metadata in store.config.json for ASO (App Store Optimization). Use when working with store.config.json, App Store keywords, titles, subtitles, descriptions, or localizing app metadata. Helps maximize app visibility and downloads.

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Use when a medical AI paper should be found and cited by AI search engines and RAG tools. Optimizes the title, abstract, summary box (Key Points, Research in Context), keywords, preprint, GitHub README/CITATION.cff and Hugging Face card, returning a visible pass/fail checklist.

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What this skill does and when to use it. Claude reads this to decide relevance. Include keywords users would naturally say.

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Design and build AI agents for any domain. Use when users: (1) ask to "create an agent", "build an assistant", or "design an AI system" (2) want to understand agent architecture, agentic patterns, or autonomous AI (3) need help with capabilities, subagents, planning, or skill mechanisms (4) ask about AI coding agents, agent runtimes, or similar internals (5) want to build agents for business, research, creative, or operational tasks Keywords: agent, assistant, autonomous, workflow, tool use, multi-step, orchestration

日本語の概要は準備中です。原文の説明を表示しています。